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Phishing Attack Detection on URLs Using KNN, RF, DT with GA and K-fold Cross Validation Approach

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  • Jun Chen Chong

    (New Era University College)

  • Nah Yi Sim

    (New Era University College)

  • Chia Wei Khoh

    (New Era University College)

  • Law Teng Yi

    (New Era University College)

Abstract

This research paper highlights a comprehensive study on phishing attack detection using machine learning algorithms which covers K-Nearest Neighbours (KNN), Random Forest and Decision Tree methods. Due to the ongoing rise of phishing attack, the needs for phishing attack detection method is necessary. This study used dataset downloaded from Kaggle, and then use various features to extract each URLs link retrieve from the dataset to generate another form of dataset for more successful detection. Next, employs K-Fold Cross-Validation methodology and Genetic Algorithms to optimise hyper parameter. The results show that both the Random Forest and Decision Tree models achieved perfect accuracy of 100%, while the KNN model achieved accuracy of 99.87%. The results underscore the effectiveness of machine learning techniques in enhancing phishing detection capabilities, contributing to improved cybersecurity measures.

Suggested Citation

  • Jun Chen Chong & Nah Yi Sim & Chia Wei Khoh & Law Teng Yi, 2025. "Phishing Attack Detection on URLs Using KNN, RF, DT with GA and K-fold Cross Validation Approach," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(1), pages 1623-1641, January.
    • Handle: RePEc:bcp:journl:v:9:y:2025:i:1:p:1623-1641
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